Generally, ASCII pictures are divided into two major categories, i.e., tone-based ASCII pictures and structure-based ASCII pictures. The tone-based ASCII pictures maintain an intensity distribution of a reference image, while the structure-based ASCII pictures capture a major structure of image content. For example, with reference to FIGS. 1(a)-1(c), a reference image in FIG. 1(a) may be represented by a tone-based ASCII picture as shown in FIG. 1(b) or a structure-based ASCII picture as shown in FIG. 1(c). Currently, there are some existing computational methods available for handling the tone-based ASCII pictures. But, currently satisfactory structure-based ASCII pictures are mostly created by hand. There is no tool capable for producing structure-based ASCII pictures in an efficient and facile way.
For the structure-based ASCII pictures, a major challenge is an inability to depict unlimited image contents with limited character shapes and the restrictive placement of characters over character grids. Two matching strategies usually employed by ASCII artists for producing structure-based ASCII pictures are shown in FIGS. 2(a)-2(c). FIG. 2(a) shows an overlapping image between an edge map of the reference image of FIG. 1(a) and the structured-based ASCII picture of FIG. 1(c). Usually, in order to raise a chance to match the reference image with appropriate characters, artists tolerate the misalignment between characters and the reference image structure as shown in FIG. 2(b), and even intelligently deform the reference image as shown in FIG. 2(c). The challenge of shape matching in ASCII art application is actually a general pattern recognition problem. In applications such as ASCII art and optical character recognition (OCR), the shape matching should be able to tolerate the misalignment and the difference in transformation (such as scale, translation and orientation) shall be also considered. For instance, in recognizing characters “O” and “o”, both the scale and the translation shall be considered; while in recognizing characters “6” and “9”, the orientation shall be considered. Unfortunately, existing shape similarity metrics used for shape matching are either alignment-sensitive or transformation-invariant, and hence not applicable.